The present invention relates to product checkout devices and more specifically to a method of combining spectral data with non-spectral data in a produce recognition system.
Bar code readers are well known for their usefulness in retail checkout and inventory control. Bar code readers are capable of identifying and recording most items during a typical transaction since most items are labeled with bar codes.
Items which are typically not identified and recorded by a bar code reader are produce items, since produce items are typically not labeled with bar codes. Bar code readers may include a scale for weighing produce items to assist in determining the price of such items. But identification of produce items is still a task for the checkout operator, who must identify a produce item and then manually enter an item identification code. Operator identification methods are slow and inefficient because they typically involve a visual comparison of a produce item with pictures of produce items, or a lookup of text in table. Operator identification methods are also prone to error, on the order of fifteen percent.
A produce recognition system is disclosed in the cited co-pending application, U.S. Ser. No. 09/189,783, now U.S. Pat. No. 6,332,573. A produce item is placed over a window in a produce data collector, the produce item is illuminated, and the spectrum of the diffuse reflected light from the produce item is measured. A terminal compares the spectrum to reference spectra in a library. The terminal determines candidate produce items and corresponding confidence levels and chooses the candidate with the highest confidence level. The terminal may additionally display the candidates for operator verification and selection.
Increases in speed and accuracy are important in a checkout environment. It would be desirable to improve the speed and accuracy of the produce recognition process by supplementing spectral data with additional information helpful to recognition. Types of data which could potentially be used to improve identification include texture data, size and shape data, weight and density data, and brightness data.
Since each data type describes a different physical attribute of an object, combining them mathematically is difficult and non-trivial. Specifically, the spectral data may consist of dozens of variables, each corresponding to a single color band, while weight and brightness, for example, may each be represented by a single variable.
Therefore, it would be desirable to provide a method of combining spectral data with non-spectral data in a produce recognition system.
In accordance with the teachings of the present invention, a method of combining spectral data with non-spectral data in a produce recognition system is provided.
A method is presented for using a defined distance measure of likeness (DML) algorithm and Bayes Rule to compute a probability of an unknown object being of a given class Ci. In particular, the DML algorithm allows the projection of any data type into a one-dimensional space, thus simplifying the multivariate conditional probability density function into an univariate function. The conditional probability densities from spectral and non-spectral data types are combined together with a priori probabilities through Bayes Rule to provide a probability estimate for each class. The resulting combination provides a statistically self-consistent estimate of the a posteriori probability for each class. A posteriori Probability values can then be used to rank the classes and to determine a subset of the most likely classes.
The method includes the steps of collecting the spectral and non-spectral data for the produce item, determining DML values between the spectral and the non-spectral data and reference produce data for a plurality of types of produce items, determining conditional probability densities for all of the types of produce items using the DML values, combining the conditional probability densities to form a combined conditional probability density, and determining probabilities for the types of produce items from the combined conditional probability density.
It is accordingly an object of the present invention to provide a method of combining spectral data with non-spectral data in a produce recognition system.
It is another object of the present invention to improve the speed and accuracy of produce recognition.
It is another object of the present invention to provide a produce recognition system and method.
It is another object of the present invention to provide a produce recognition system and method which combining spectral data with non-spectral data.
It is another object of the present invention to provide a produce recognition system and method which combines spectral data with non-spectral data using a distance measure of likeness (DML) value.
It is another object of the present invention to provide a produce recognition system and method which combines spectral data with non-spectral data and which identifies produce items by sorting the distance measure of likeness (DML) values in ascending order and choosing the item with smallest distance as the most likely identification.